方法对比
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| 自监督隔离森林× | 孤立森林 (Isolation Forest)× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2008–2020s | 2008 |
| 提出者≠ | Liu, F. T., Ting, K. M., & Zhou, Z.-H. (iForest); SSL extensions by multiple authors | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| 类型≠ | Ensemble anomaly detector with self-supervised pre-training | Unsupervised ensemble (random partitioning trees) |
| 开创性文献≠ | Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM), pp. 413–422. DOI ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| 别名≠ | SSL Isolation Forest, self-supervised iForest, semi-supervised isolation forest, contrastive isolation forest | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| 相关≠ | 4 | 5 |
| 摘要≠ | Self-supervised Isolation Forest augments the classic Isolation Forest anomaly detector with a self-supervised pre-training stage. A pretext task — such as predicting rotation, masked features, or contrastive pairs — is solved without labels to learn a richer feature representation, which is then used when building the isolation trees, yielding sharper anomaly scores on complex, high-dimensional tabular data. | Isolation Forest is an unsupervised machine-learning method for anomaly and outlier detection, introduced by Liu, Ting and Zhou in 2008, that isolates anomalies through random partitioning of the data. It works without any labelled anomaly data and scales to high-dimensional datasets. |
| ScholarGate数据集 ↗ |
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